Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness, since biases are often encoded in target variable definition itself, before any data collection or training. The downstream impacts of target variable definitions must be taken into account in order to responsibly develop, deploy, and use the algorithmic systems. We propose FairTargetSim (FTS), an interactive and simulations-based approach for this. We demonstrate FTS using the example of algorithmic hiring, grounded in real-world data and user-defined target variables. FTS is open-source; it can be used by algorithm developers, non-technical stakeholders, researchers, and educators in a number of ways. FTS is available at: http://tinyurl.com/ftsinterface. The video accompanying this paper is here: http://tinyurl.com/ijcaifts.
论文检索
输入标题、作者或关键词，从 1,280 篇学术成果中精准定位
This paper presents a multimodal intelligent dialogue system that seamlessly integrates document analysis, visual media processing, and audio interaction within a unified web interface. The system ensures secure user identity verification through persistent conversational management, leveraging textual document analysis, dynamic context integration, and cross-media interactions via video, image, and real-time speech processing. Our approach introduces three key innovations: (1) context-aware document analysis through text extraction, (2) a multimodal input pipeline supporting images, videos, and audio, and (3) persistent chat history management for maintaining conversational continuity. The system facilitates seamless transitions between audio and text, enabling natural interactions by processing audio input and converting text responses into speech. Additionally, the platform provides an intuitive interface for document uploads, camera capture, and audio recording, while ensuring conversation context is preserved across sessions. This implementation demonstrates the practical integration of multimodal input in an interactive artificial intelligence (AI) system, showcasing its potential for enhanced user engagement and interaction.
Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence, enabling automated extraction of key information from complex documents across domains such as medical, financial, and educational applications. However, form-like documents pose unique challenges due to their complex layouts, multi-stakeholder involvement, and high structural variability. Addressing these issues, the VRD-IU Competition was introduced, focusing on extracting and localizing key information from multi-format forms within the Form-NLU dataset, which includes digital, printed, and handwritten documents. This paper presents insights from the competition, which featured two tracks: Track A, emphasizing entity-based key information retrieval, and Track B, targeting end-to-end key information localization from raw document images. With over 20 participating teams, the competition showcased various state-of-the-art methodologies, including hierarchical decomposition, transformer-based retrieval, multimodal feature fusion, and advanced object detection techniques. The top-performing models set new benchmarks in VRDU, providing valuable insights into document intelligence.
Despite an ongoing shift in automated chemical literature search methods, many are fairly limited in ability to find very specific relevant information about a drawn molecule and its associated property data. We aim to tackle the challenge of converting drawn molecules to a machine readable representation and co-reference any associated molecule data. MoleculeMiner is a system where a user can feed in their own patent or paper to obtain each drawn molecule along with any specific metadata (chemical name, chemical reactivity, yield, purity etc.) provided anywhere in the PDF in a tabular format, using an interactive user-friendly environment. We also present MolScribeV2, a molecular image parser which improved upon the original MolScribe by introducing pixel-based self attention positional embedding technique. Along with other changes, MolScribeV2 is robust to varied styles of compound drawings commonly found in patents and papers--scanned or born digital. Our extraction and user interactive system can be found at https://github.com/insitro/MoleculeMiner.
Large Language Models (LLMs) have become integral to human decision-making processes. However, their outputs are not always reliable, often requiring users to assess the accuracy of the information provided manually. This issue is exacerbated by hallucinated responses, which are frequently presented with convincing but incorrect explanations, leading to trust concerns among users. To address this challenge, we propose GE-Chat, a knowledge Graph-enhanced retrieval-augmented generation framework designed to deliver Evidence-based responses. Specifically, when users upload a document, GE-Chat constructs a knowledge graph to support a retrieval-augmented agent, enriching the agent's responses with external knowledge beyond its training data. We further incorporate Chain-of-Thought (CoT) reasoning, n-hop subgraph searching, and entailment-based sentence generation to ensure accurate evidence retrieval. Experimental results demonstrate that our approach improves the ability of existing models to identify precise evidence in free-form contexts, offering a reliable mechanism for verifying LLM-generated conclusions and enhancing trustworthiness.
Machine unlearning, the process of removing specific data influences from Machine Learning models, is critical for complying with regulations like the GDPR's right to be forgotten and addressing copyright disputes in large models. Despite its rising importance, the field still lacks standardized tools, hindering reproducibility and evaluation. Here, we present, in an extensive way, ERASURE, a unified framework enabling reproducibility by implementing common unlearning techniques, evaluation metrics, and dedicated datasets. ERASURE advances research, ensures solution comparability, and facilitates reproducibility, addressing future legal and ethical challenges in data management.
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.
In the world of audio narration and video production, maintaining clear and accurate dialogue is crucial. However, most work done in dubbing mistakes is done in post-production which is often not applicable to live broadcasts. This project aims to develop a real-time voice correction system that automatically detects and corrects speech errors in near real-time while integrating the adjusted audio into ongoing conversations without disrupting the natural flow. This paper utilizes various AI tools like the Nous Hermes 2-Mistral 7B DPO large language model to first generate the reference script for Coqui's XTTS-V2 zero-shot text-to-speech voice cloning model. After the correction is generated, it goes through a series of filters to replace the mistake and seamlessly integrates it. The experiment's user survey demonstrates that the corrected audio is of high quality.
TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records
PDF ↗Timely understanding of a patient’s clinical course is crucial for effective treatment. Extracting course-related information, such as temporal and medical events, from unstructured medical records is both challenging and time-consuming, especially when relying on manual identification by physicians. We introduce TimelyMed, a system powered by a locally deployed large language model (LLM) that ensures data security while efficiently organizing key psychiatric events and their corresponding temporal information. Additionally, our system is attributed, allowing clinicians to not only categorize events but also trace them back to their original textual descriptions, ensuring transparency and interpretability in clinical decision-making. By organizing temporal and medical event information into timelines, our system enables physicians to quickly grasp a patient’s medical history while effectively reducing their cognitive burden.
We formulate a Coverage Path Planning (CPP) problem for a helicopter or a UAV tasked with mapping ground-level radiation while avoiding radiation that is too strong. We introduce a simulation environment that incorporates digital elevation models, altitude-dependent measurement footprints and realistic flight constraints, as well as state-of-the-art radiation scenario simulations, such as nuclear explosions, provided by the German Federal Office for Radiation Protection. We highlight the complexity of radiological survey missions and demonstrate the necessity for new CPP approaches that address these unique challenges. The code to our simulation environment can be found under https://github.com/JohannBlake/Aerial-Coverage-Path-Planning-in-Nuclear-Emergencies.
In this demonstration, we present a system that automates regression testing for video games using automated planning techniques. Traditional test scripts are a common method for testing both video games and software in general. While effective, they require manual creation and frequent updates throughout development, making the process labor-intensive. Our system eliminates this burden by automatically generating and maintaining test scripts. The test engineer only needs to define the game’s rules using the Planning Domain Definition Language (PDDL) and specify initial states and goals for individual test cases. This significantly reduces human effort while ensuring test scripts remain up to date. Additionally, our system integrates with game engine editors—supporting both Unity and Unreal to execute and evaluate test cases directly within the game. It collects detailed logs, telemetry data, and video recordings, allowing users to review test results efficiently.
Combining Code Generating Large Language Models and Self-Play to Iteratively Refine Strategies in Games
PDF ↗We propose a self-play approach to generating strategies for playing in multi-player games, where strategies are represented as computer code. We use large language models (LLMs) to generate pieces of code to play in the game, which we refer to as generated bots. We engage the LLM generated bots in competitions, designed to generate increasingly stronger strategies. We follow game theoretic principles in organizing these tournaments, and use a Policy Space Response Oracle (PSRO) approach. We start with an initial set of LLM generated bots, and continue in rounds for adding new bots into the population. Each round adds a bot to the population by asking the LLM to produce code for playing against a bot representing the Nash equilibrium mixture over the current population. Our analysis shows that even a few rounds are sufficient to produces strong bots for playing the game. Our demo shows the process for the game of Checkers. We allow users to select initial bots in the population, run the process, inspect how the bots evolve over time, and play against the generated bots.
Taking STEPS Forward: Enhancing Online Peer-Counseling with Schema Therapy via Socratic Questioning
PDF ↗Peer-counseling is essential in online mental health communities to provide relatable support to those seeking help, but the peer-counselors often lack professional training in therapeutic counseling to produce the desired cognitive changes. In this paper, we present STEPS, an AI-powered assistive dialog tool for peer-counseling. Unlike other existing tools, STEPS assists peer-counselors in facilitating cognitive change in online counseling settings. Towards this goal, we emulate two key phases in a Schema Therapy-based in-person counseling session–(1) Schema Assessment to uncover the deep-seated irrational beliefs underlying an individual’s mental health problems, and (2) Cognitive Change to reframe these beliefs into healthier alternatives. In both phases, we employ Socratic questioning techniques to effectively elicit critical introspection and guide cognitive change. We describe STEPS and present expert evaluation studies on its counseling conversations on real-world mental health forum posts. Our results indicate that STEPS significantly outperforms competitive baselines on all key metrics related to schema assessment, cognitive change strategies, and critical thinking, achieving an impressive average rating of 5 out of 6, highlighting its strong potential as a transformative tool for online peer-counseling.
ASP Chef enriches Answer Set Programming (ASP) with the notion of recipe, that is, a sequence of operations on answer sets. Recipes are designed and executed in modern browsers, and further improve the fast prototyping capabilities of ASP. This paper introduces new operations designed to integrate Large Language Models (LLMs) in recipe, with the aim of combining the reasoning strength of ASP with the natural language capabilities of LLMs, to enable more interactive and adaptive problem-solving workflows. In a nutshell, answer sets in input are transformed into prompts for LLMs, whose responses are processed to extract facts for subsequent operations within the recipe.
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains
PDF ↗DAVE is a framework for assisting the analysis of documents in knowledge-intensive domains, based on an entity-centric approach supported by annotations of named entities in the documents. DAVE supports search & filtering, document exploration, question answering, and knowledge refinement. It is released as an open-source project that the community can further develop. DAVE’s distinguishing features are: the integration of a chatbot interface based on recent RAG solutions into well-established entity-powered faceted search, the fusion of search and filtering features provided by entity-level annotations with the capability to ask questions on annotated documents; human-in-the-loop functions to consolidate knowledge while exploring information, allowing users to improve annotations from NLP algorithms.
Time series modeling is essential in finance, healthcare, and environmental science, yet nonlinear patterns, noise, and concept drift pose challenges. Although deep learning models, such as Transformer-based and recent pre-trained models, have achieved good performance across various time series tasks, they often lack interpretability, especially in co-evolving time series. This work introduces a kernel representation learning (KRL) perspective, rethinking time series modeling through kernel-induced self-representation to effectively capture temporal structures and dynamic transitions. Additionally, we establish theoretical connections between KRL and advanced deep-network models, demonstrating how kernel methods provide a principled approach to capturing complex time series behaviors.
Reward Adaptation via Q-Manipulation: Provably Beneficial Reward Function Transfer in Reinforcement Learning
PDF ↗Reinforcement Learning has made great strides in game playing and robotics but faces challenges with sample complexity and generalization. Transfer learning, which allows agents to reuse knowledge from prior tasks, offers a promising solution. My current research focuses on Reward Adaptation, where agents adjust to new reward functions while leveraging knowledge from tasks with different reward functions. I propose Q-Manipulation (Q-M), a method that adapts Q-functions to new rewards by computing and iteratively tightening bounds, akin to value iteration. This allows for action pruning before learning begins, enhancing sample efficiency without compromising policy optimality. Through empirical comparisons I demonstrate its effectiveness, generalizability, and practicality. Future work will handle changes in transition dynamics and continuous MDPs.
Feature attribution methods explain model predictions by assigning importance to input features. Choosing the most suitable method is difficult due to inconsistencies and a lack of universal solutions. Our research focuses on proposing tools and methodologies for evaluating, comparing, and selecting these methods.
AI models are increasingly accessible through chatbots and other applications, but their black-box nature and sensitivity to small changes in the input make them hard to interpret and trust. Existing correlation-based robustness metrics fail to explain model errors or isolate causal effects. To address this, I propose ARC (AI Rating through Causality), a causally-grounded framework for rating AI models based on their robustness. ARC evaluates robustness by quantifying statistical and confounding biases, as well as the impact of perturbations on model performance across diverse tasks. ARC produces interpretable raw scores and ratings, helping developers and users make informed decisions about model robustness. Two future directions include: (1) deriving raw scores for composite models from their component scores, and (2) combining ratings with traditional explainable AI approaches to provide a more holistic view of model behavior.
Bias towards people experiencing homelessness (PEH) is prevalent in online spaces. I leverage natural language processing (NLP) and large language models (LLMs) to identify, classify, and measure bias using geolocalized data collected from X (formerly Twitter), Reddit, meeting minutes, and news articles across the United States. The results of the study aim to provide a new path to alleviate homelessness by unveiling the intersectional bias that affects PEH. My research delivers a lexicon on homelessness, compiles an annotated database for homelessness and homelessness-racism intersectional (HRI) bias, evaluates LLMs as classifiers against these biases, and audits existing LLMs on HRI. My goal is to contribute to homelessness alleviation by counteracting social stigma and restoring the human dignity of the persons affected.